skill-scanner
Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.
Best use case
skill-scanner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.
Teams using skill-scanner should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/skill-scanner/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How skill-scanner Compares
| Feature / Agent | skill-scanner | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill Security Scanner
Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.
**Important**: Run all scripts from the repository root using the full path via `${CLAUDE_SKILL_ROOT}`.
## When to Use
- You need to evaluate a skill for prompt injection, malicious code, over-broad permissions, or supply-chain risk before adopting it.
- You want a static scan plus manual review workflow for a skill directory.
- The task is to decide whether a skill is safe enough to trust in an agent environment.
## Bundled Script
### `scripts/scan_skill.py`
Static analysis scanner that detects deterministic patterns. Outputs structured JSON.
```bash
uv run ${CLAUDE_SKILL_ROOT}/scripts/scan_skill.py <skill-directory>
```
Returns JSON with findings, URLs, structure info, and severity counts. The script catches patterns mechanically — your job is to evaluate intent and filter false positives.
## Workflow
### Phase 1: Input & Discovery
Determine the scan target:
- If the user provides a skill directory path, use it directly
- If the user names a skill, look for it under `plugins/*/skills/<name>/` or `.claude/skills/<name>/`
- If the user says "scan all skills", discover all `*/SKILL.md` files and scan each
Validate the target contains a `SKILL.md` file. List the skill structure:
```bash
ls -la <skill-directory>/
ls <skill-directory>/references/ 2>/dev/null
ls <skill-directory>/scripts/ 2>/dev/null
```
### Phase 2: Automated Static Scan
Run the bundled scanner:
```bash
uv run ${CLAUDE_SKILL_ROOT}/scripts/scan_skill.py <skill-directory>
```
Parse the JSON output. The script produces findings with severity levels, URL analysis, and structure information. Use these as leads for deeper analysis.
**Fallback**: If the script fails, proceed with manual analysis using Grep patterns from the reference files.
### Phase 3: Frontmatter Validation
Read the SKILL.md and check:
- **Required fields**: `name` and `description` must be present
- **Name consistency**: `name` field should match the directory name
- **Tool assessment**: Review `allowed-tools` — is Bash justified? Are tools unrestricted (`*`)?
- **Model override**: Is a specific model forced? Why?
- **Description quality**: Does the description accurately represent what the skill does?
### Phase 4: Prompt Injection Analysis
Load `${CLAUDE_SKILL_ROOT}/references/prompt-injection-patterns.md` for context.
Review scanner findings in the "Prompt Injection" category. For each finding:
1. Read the surrounding context in the file
2. Determine if the pattern is **performing** injection (malicious) or **discussing/detecting** injection (legitimate)
3. Skills about security, testing, or education commonly reference injection patterns — this is expected
**Critical distinction**: A security review skill that lists injection patterns in its references is documenting threats, not attacking. Only flag patterns that would execute against the agent running the skill.
### Phase 5: Behavioral Analysis
This phase is agent-only — no pattern matching. Read the full SKILL.md instructions and evaluate:
**Description vs. instructions alignment**:
- Does the description match what the instructions actually tell the agent to do?
- A skill described as "code formatter" that instructs the agent to read ~/.ssh is misaligned
**Config/memory poisoning**:
- Instructions to modify `CLAUDE.md`, `MEMORY.md`, `settings.json`, `.mcp.json`, or hook configurations
- Instructions to add itself to allowlists or auto-approve permissions
- Writing to `~/.claude/` or any agent configuration directory
**Scope creep**:
- Instructions that exceed the skill's stated purpose
- Unnecessary data gathering (reading files unrelated to the skill's function)
- Instructions to install other skills, plugins, or dependencies not mentioned in the description
**Information gathering**:
- Reading environment variables beyond what's needed
- Listing directory contents outside the skill's scope
- Accessing git history, credentials, or user data unnecessarily
### Phase 6: Script Analysis
If the skill has a `scripts/` directory:
1. Load `${CLAUDE_SKILL_ROOT}/references/dangerous-code-patterns.md` for context
2. Read each script file fully (do not skip any)
3. Check scanner findings in the "Malicious Code" category
4. For each finding, evaluate:
- **Data exfiltration**: Does the script send data to external URLs? What data?
- **Reverse shells**: Socket connections with redirected I/O
- **Credential theft**: Reading SSH keys, .env files, tokens from environment
- **Dangerous execution**: eval/exec with dynamic input, shell=True with interpolation
- **Config modification**: Writing to agent settings, shell configs, git hooks
5. Check PEP 723 `dependencies` — are they legitimate, well-known packages?
6. Verify the script's behavior matches the SKILL.md description of what it does
**Legitimate patterns**: `gh` CLI calls, `git` commands, reading project files, JSON output to stdout are normal for skill scripts.
### Phase 7: Supply Chain Assessment
Review URLs from the scanner output and any additional URLs found in scripts:
- **Trusted domains**: GitHub, PyPI, official docs — normal
- **Untrusted domains**: Unknown domains, personal sites, URL shorteners — flag for review
- **Remote instruction loading**: Any URL that fetches content to be executed or interpreted as instructions is high risk
- **Dependency downloads**: Scripts that download and execute binaries or code at runtime
- **Unverifiable sources**: References to packages or tools not on standard registries
### Phase 8: Permission Analysis
Load `${CLAUDE_SKILL_ROOT}/references/permission-analysis.md` for the tool risk matrix.
Evaluate:
- **Least privilege**: Are all granted tools actually used in the skill instructions?
- **Tool justification**: Does the skill body reference operations that require each tool?
- **Risk level**: Rate the overall permission profile using the tier system from the reference
Example assessments:
- `Read Grep Glob` — Low risk, read-only analysis skill
- `Read Grep Glob Bash` — Medium risk, needs Bash justification (e.g., running bundled scripts)
- `Read Grep Glob Bash Write Edit WebFetch Task` — High risk, near-full access
## Confidence Levels
| Level | Criteria | Action |
|-------|----------|--------|
| **HIGH** | Pattern confirmed + malicious intent evident | Report with severity |
| **MEDIUM** | Suspicious pattern, intent unclear | Note as "Needs verification" |
| **LOW** | Theoretical, best practice only | Do not report |
**False positive awareness is critical.** The biggest risk is flagging legitimate security skills as malicious because they reference attack patterns. Always evaluate intent before reporting.
## Output Format
```markdown
## Skill Security Scan: [Skill Name]
### Summary
- **Findings**: X (Y Critical, Z High, ...)
- **Risk Level**: Critical / High / Medium / Low / Clean
- **Skill Structure**: SKILL.md only / +references / +scripts / full
### Findings
#### [SKILL-SEC-001] [Finding Type] (Severity)
- **Location**: `SKILL.md:42` or `scripts/tool.py:15`
- **Confidence**: High
- **Category**: Prompt Injection / Malicious Code / Excessive Permissions / Secret Exposure / Supply Chain / Validation
- **Issue**: [What was found]
- **Evidence**: [code snippet]
- **Risk**: [What could happen]
- **Remediation**: [How to fix]
### Needs Verification
[Medium-confidence items needing human review]
### Assessment
[Safe to install / Install with caution / Do not install]
[Brief justification for the assessment]
```
**Risk level determination**:
- **Critical**: Any high-confidence critical finding (prompt injection, credential theft, data exfiltration)
- **High**: High-confidence high-severity findings or multiple medium findings
- **Medium**: Medium-confidence findings or minor permission concerns
- **Low**: Only best-practice suggestions
- **Clean**: No findings after thorough analysis
## Reference Files
| File | Purpose |
|------|---------|
| `references/prompt-injection-patterns.md` | Injection patterns, jailbreaks, obfuscation techniques, false positive guide |
| `references/dangerous-code-patterns.md` | Script security patterns: exfiltration, shells, credential theft, eval/exec |
| `references/permission-analysis.md` | Tool risk tiers, least privilege methodology, common skill permission profiles |
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.Related Skills
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